OpenRaiser/Intern-Atlas huggingface dataset:
Differentiating Approach and Avoidance from Traditional Notions of Sentiment in Economic Contexts
Venue: arXiv cs.CL 2021
Year: 2021

There is growing interest in the role of sentiment in economic decision-making. However, most research on the subject has focused on positive and negative valence. Conviction Narrative Theory (CNT) places Approach and Avoidance sentiment (that which drives action) at the heart of real-world decision-making, and argues that it better captures emotion in financial markets. This research, bringing together psychology and machine learning, introduces new techniques to differentiate Approach and Avoidance from positive and negative sentiment on a fundamental level of meaning. It does this by comparing word-lists, previously constructed to capture these concepts in text data, across a large range of semantic features. The results demonstrate that Avoidance in particular is well defined as a separate type of emotion, which is evaluative/cognitive and action-orientated in nature. Refining the Avoidance word-list according to these features improves macroeconomic models, suggesting that they capture the essence of Avoidance and that it plays a crucial role in driving real-world economic decision-making.
Ensemble and Mixed Learning Techniques for Credit Card Fraud Detection
Venue: arXiv cs.LG 2021
Year: 2021

Spurious credit card transactions are a significant source of financial losses and urge the development of accurate fraud detection algorithms. In this paper, we use machine learning strategies for such an aim. First, we apply a mixed learning technique that uses K-means preprocessing before trained classification to the problem at hand. Next, we introduce an adapted detector ensemble technique that uses OR-logic algorithm aggregation to enhance the detection rate. Then, both strategies are deployed in tandem in numerical simulations using real-world transactions data. We observed from simulation results that the proposed methods diminished computational cost and enhanced performance concerning state-of-the-art techniques.
VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning
Venue: arXiv cs.SE 2021
Year: 2021

Variable names are critical for conveying intended program behavior. Machine learning-based program analysis methods use variable name representations for a wide range of tasks, such as suggesting new variable names and bug detection. Ideally, such methods could capture semantic relationships between names beyond syntactic similarity, e.g., the fact that the names average and mean are similar. Unfortunately, previous work has found that even the best of previous representation approaches primarily capture relatedness (whether two variables are linked at all), rather than similarity (whether they actually have the same meaning). We propose VarCLR, a new approach for learning semantic representations of variable names that effectively captures variable similarity in this stricter sense. We observe that this problem is an excellent fit for contrastive learning, which aims to minimize the distance between explicitly similar inputs, while maximizing the distance between dissimilar inputs. This requires labeled training data, and thus we construct a novel, weakly-supervised variable renaming dataset mined from GitHub edits. We show that VarCLR enables the effective application of sophisticated, general-purpose language models like BERT, to variable name representation and thus also to related downstream tasks like variable name similarity search or spelling correction. VarCLR produces models that significantly outperform the state-of-the-art on IdBench, an existing benchmark that explicitly captures variable similarity (as distinct from relatedness). Finally, we contribute a release of all data, code, and pre-trained models, aiming to provide a drop-in replacement for variable representations used in either existing or future program analyses that rely on variable names.
ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting
Venue: arXiv cs.LG 2021
Year: 2021

Short-term load forecasting (STLF) is challenging due to complex time series (TS) which express three seasonal patterns and a nonlinear trend. This paper proposes a novel hybrid hierarchical deep learning model that deals with multiple seasonality and produces both point forecasts and predictive intervals (PIs). It combines exponential smoothing (ES) and a recurrent neural network (RNN). ES extracts dynamically the main components of each individual TS and enables on-the-fly deseasonalization, which is particularly useful when operating on a relatively small data set. A multi-layer RNN is equipped with a new type of dilated recurrent cell designed to efficiently model both short and long-term dependencies in TS. To improve the internal TS representation and thus the model's performance, RNN learns simultaneously both the ES parameters and the main mapping function transforming inputs into forecasts. We compare our approach against several baseline methods, including classical statistical methods and machine learning (ML) approaches, on STLF problems for 35 European countries. The empirical study clearly shows that the proposed model has high expressive power to solve nonlinear stochastic forecasting problems with TS including multiple seasonality and significant random fluctuations. In fact, it outperforms both statistical and state-of-the-art ML models in terms of accuracy.
Learning Query Expansion over the Nearest Neighbor Graph
Venue: arXiv cs.CV 2021
Year: 2021

Query Expansion (QE) is a well established method for improving retrieval metrics in image search applications. When using QE, the search is conducted on a new query vector, constructed using an aggregation function over the query and images from the database. Recent works gave rise to QE techniques in which the aggregation function is learned, whereas previous techniques were based on hand-crafted aggregation functions, e.g., taking the mean of the query's nearest neighbors. However, most QE methods have focused on aggregation functions that work directly over the query and its immediate nearest neighbors. In this work, a hierarchical model, Graph Query Expansion (GQE), is presented, which is learned in a supervised manner and performs aggregation over an extended neighborhood of the query, thus increasing the information used from the database when computing the query expansion, and using the structure of the nearest neighbors graph. The technique achieves state-of-the-art results over known benchmarks.
Globalization of Scientific Communication: Evidence from authors in academic journals by country of origin
Venue: arXiv cs.DL 2021
Year: 2021

This study measures the tendency to publish in international scientific journals. For each of nearly 35 thousands Scopus-indexed journals, we derive seven globalization indicators based on the composition of authors by country of origin and other characteristics. These are subsequently scaled up to the level of 174 countries and 27 disciplines between 2005 and 2017. The results indicate that advanced countries maintain high globalization of scientific communication that is not varying across disciplines. Social sciences and health sciences are less globalized than physical and life sciences. Countries of the former Soviet bloc score far lower on the globalization measures, especially in social sciences or health sciences. Russia remains among the least globalized during the whole period, with no upward trend. Contrary, China has profoundly globalized its science system, gradually moving from the lowest globalization figures to the world average. The paper concludes with reflections on measurement issues and policy implications.
A Dataset of Stationary, Fixed-wing Aircraft on a Collision Course for Vision-Based Sense and Avoid
Venue: arXiv cs.RO 2021
Year: 2021

The emerging global market for unmanned aerial vehicle (UAV) services is anticipated to reach USD 58.4 billion by 2026, spurring significant efforts to safely integrate routine UAV operations into the national airspace in a manner that they do not compromise the existing safety levels. The commercial use of UAVs would be enhanced by an ability to sense and avoid potential mid-air collision threats however research in this field is hindered by the lack of available datasets as they are expensive and technically complex to capture. In this paper we present a dataset for vision based aircraft detection. The dataset consists of 15 image sequences containing 55,521 images of a fixed-wing aircraft approaching a stationary, grounded camera. Ground truth labels and a performance benchmark are also provided. To our knowledge, this is the first public dataset for studying medium sized, fixed-wing aircraft on a collision course with the observer. The full dataset and ground truth labels are publicly available at https://qcr.github.io/dataset/aircraft-collision-course/.
CDGNet: A Cross-Time Dynamic Graph-based Deep Learning Model for Traffic Forecasting
Venue: arXiv cs.LG 2021
Year: 2021

Traffic forecasting is important in intelligent transportation systems of webs and beneficial to traffic safety, yet is very challenging because of the complex and dynamic spatio-temporal dependencies in real-world traffic systems. Prior methods use the pre-defined or learnable static graph to extract spatial correlations. However, the static graph-based methods fail to mine the evolution of the traffic network. Researchers subsequently generate the dynamic graph for each time slice to reflect the changes of spatial correlations, but they follow the paradigm of independently modeling spatio-temporal dependencies, ignoring the cross-time spatial influence. In this paper, we propose a novel cross-time dynamic graph-based deep learning model, named CDGNet, for traffic forecasting. The model is able to effectively capture the cross-time spatial dependence between each time slice and its historical time slices by utilizing the cross-time dynamic graph. Meanwhile, we design a gating mechanism to sparse the cross-time dynamic graph, which conforms to the sparse spatial correlations in the real world. Besides, we propose a novel encoder-decoder architecture to incorporate the cross-time dynamic graph-based GCN for multi-step traffic forecasting. Experimental results on three real-world public traffic datasets demonstrate that CDGNet outperforms the state-of-the-art baselines. We additionally provide a qualitative study to analyze the effectiveness of our architecture.
Unfairness Despite Awareness: Group-Fair Classification with Strategic Agents
Venue: arXiv cs.MA 2021
Year: 2021

The use of algorithmic decision making systems in domains which impact the financial, social, and political well-being of people has created a demand for these decision making systems to be "fair" under some accepted notion of equity. This demand has in turn inspired a large body of work focused on the development of fair learning algorithms which are then used in lieu of their conventional counterparts. Most analysis of such fair algorithms proceeds from the assumption that the people affected by the algorithmic decisions are represented as immutable feature vectors. However, strategic agents may possess both the ability and the incentive to manipulate this observed feature vector in order to attain a more favorable outcome. We explore the impact that strategic agent behavior could have on fair classifiers and derive conditions under which this behavior leads to fair classifiers becoming less fair than their conventional counterparts under the same measure of fairness that the fair classifier takes into account. These conditions are related to the the way in which the fair classifier remedies unfairness on the original unmanipulated data: fair classifiers which remedy unfairness by becoming more selective than their conventional counterparts are the ones that become less fair than their counterparts when agents are strategic. We further demonstrate that both the increased selectiveness of the fair classifier, and consequently the loss of fairness, arises when performing fair learning on domains in which the advantaged group is overrepresented in the region near (and on the beneficial side of) the decision boundary of conventional classifiers. Finally, we observe experimentally, using several datasets and learning methods, that this fairness reversal is common, and that our theoretical characterization of the fairness reversal conditions indeed holds in most such cases.
Review of Data Integrity Attacks and Mitigation Methods in Edge computing
Venue: arXiv cs.CR 2021
Year: 2021

In recent years, edge computing has emerged as a promising technology due to its unique feature of real-time computing and parallel processing. They provide computing and storage capacity closer to the data source and bypass the distant links to the cloud. The edge data analytics process the ubiquitous data on the edge layer to offer real-time interactions for the application. However, this process can be prone to security threats like gaining malicious access or manipulating sensitive data. This can lead to the intruder's control, alter, or add erroneous data affecting the integrity and data analysis efficiency. Due to the lack of transparency of stakeholders processing edge data, it is challenging to identify the vulnerabilities. Many reviews are available on data security issues on the edge layer; however, they do not address integrity issues exclusively. Therefore, this paper concentrates only on data integrity threats that directly influence edge data analysis. Further shortcomings in existing work are identified with few research directions.
Texture Reformer: Towards Fast and Universal Interactive Texture Transfer
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we present the texture reformer, a fast and universal neural-based framework for interactive texture transfer with user-specified guidance. The challenges lie in three aspects: 1) the diversity of tasks, 2) the simplicity of guidance maps, and 3) the execution efficiency. To address these challenges, our key idea is to use a novel feed-forward multi-view and multi-stage synthesis procedure consisting of I) a global view structure alignment stage, II) a local view texture refinement stage, and III) a holistic effect enhancement stage to synthesize high-quality results with coherent structures and fine texture details in a coarse-to-fine fashion. In addition, we also introduce a novel learning-free view-specific texture reformation (VSTR) operation with a new semantic map guidance strategy to achieve more accurate semantic-guided and structure-preserved texture transfer. The experimental results on a variety of application scenarios demonstrate the effectiveness and superiority of our framework. And compared with the state-of-the-art interactive texture transfer algorithms, it not only achieves higher quality results but, more remarkably, also is 2-5 orders of magnitude faster. Code is available at https://github.com/EndyWon/Texture-Reformer.
ML Attack Models: Adversarial Attacks and Data Poisoning Attacks
Venue: arXiv cs.LG 2021
Year: 2021

Many state-of-the-art ML models have outperformed humans in various tasks such as image classification. With such outstanding performance, ML models are widely used today. However, the existence of adversarial attacks and data poisoning attacks really questions the robustness of ML models. For instance, Engstrom et al. demonstrated that state-of-the-art image classifiers could be easily fooled by a small rotation on an arbitrary image. As ML systems are being increasingly integrated into safety and security-sensitive applications, adversarial attacks and data poisoning attacks pose a considerable threat. This chapter focuses on the two broad and important areas of ML security: adversarial attacks and data poisoning attacks.
Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook
Venue: arXiv cs.CV 2021
Year: 2021

Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems. It entails estimating the trajectory of the target in an image sequence, given only its initial location, and segmentation, or its rough approximation in the form of a bounding box. Discriminative Correlation Filters (DCFs) and deep Siamese Networks (SNs) have emerged as dominating tracking paradigms, which have led to significant progress. Following the rapid evolution of visual object tracking in the last decade, this survey presents a systematic and thorough review of more than 90 DCFs and Siamese trackers, based on results in nine tracking benchmarks. First, we present the background theory of both the DCF and Siamese tracking core formulations. Then, we distinguish and comprehensively review the shared as well as specific open research challenges in both these tracking paradigms. Furthermore, we thoroughly analyze the performance of DCF and Siamese trackers on nine benchmarks, covering different experimental aspects of visual tracking: datasets, evaluation metrics, performance, and speed comparisons. We finish the survey by presenting recommendations and suggestions for distinguished open challenges based on our analysis.
Optimizing Write Fidelity of MRAMs via Iterative Water-filling Algorithm
Venue: arXiv cs.ET 2021
Year: 2021

Magnetic random-access memory (MRAM) is a promising memory technology due to its high density, non-volatility, and high endurance. However, achieving high memory fidelity incurs significant write-energy costs, which should be reduced for large-scale deployment of MRAMs. In this paper, we formulate a \emph{biconvex} optimization problem to optimize write fidelity given energy and latency constraints. The basic idea is to allocate non-uniform write pulses depending on the importance of each bit position. The fidelity measure we consider is mean squared error (MSE), for which we optimize write pulses via \emph{alternating convex search (ACS)}. By using Karush-Kuhn-Tucker (KKT) conditions, we derive analytic solutions and propose an \emph{iterative water-filling-type} algorithm by leveraging the analytic solutions. Hence, the proposed iterative water-filling algorithm is computationally more efficient than the original ACS while their solutions are identical. Although the original ACS and the proposed iterative water-filling algorithm do not guarantee global optimality, the MSEs obtained by the proposed algorithm are comparable to the MSEs by complicated global nonlinear programming solvers. Furthermore, we prove that the proposed algorithm can reduce the MSE exponentially with the number of bits per word. For an 8-bit accessed word, the proposed algorithm reduces the MSE by a factor of 21. We also evaluate the proposed algorithm for MNIST dataset classification supposing that the model parameters of deep neural networks are stored in MRAMs. The numerical results show that the optimized write pulses can achieve \SI{40}{\%} write energy reduction for a given classification accuracy.
Hyperstyle: A Tool for Assessing the Code Quality of Solutions to Programming Assignments
Venue: arXiv cs.SE 2021
Year: 2021

In software engineering, it is not enough to simply write code that only works as intended, even if it is free from vulnerabilities and bugs. Every programming language has a style guide and a set of best practices defined by its community, which help practitioners to build solutions that have a clear structure and therefore are easy to read and maintain. To introduce assessment of code quality into the educational process, we developed a tool called Hyperstyle. To make it reflect the needs of the programming community and at the same time be easily extendable, we built it upon several existing professional linters and code checkers. Hyperstyle supports four programming languages (Python, Java, Kotlin, and Javascript) and can be used as a standalone tool or integrated into a MOOC platform. We have integrated the tool into two educational platforms, Stepik and JetBrains Academy, and it has been used to process about one million submissions every week since May 2021.
CU-UD: text-mining drug and chemical-protein interactions with ensembles of BERT-based models
Venue: arXiv cs.CL 2021
Year: 2021

Identifying the relations between chemicals and proteins is an important text mining task. BioCreative VII track 1 DrugProt task aims to promote the development and evaluation of systems that can automatically detect relations between chemical compounds/drugs and genes/proteins in PubMed abstracts. In this paper, we describe our submission, which is an ensemble system, including multiple BERT-based language models. We combine the outputs of individual models using majority voting and multilayer perceptron. Our system obtained 0.7708 in precision and 0.7770 in recall, for an F1 score of 0.7739, demonstrating the effectiveness of using ensembles of BERT-based language models for automatically detecting relations between chemicals and proteins. Our code is available at https://github.com/bionlplab/drugprot_bcvii.
Unsupervised Law Article Mining based on Deep Pre-Trained Language Representation Models with Application to the Italian Civil Code
Venue: arXiv cs.CL 2021
Year: 2021

Modeling law search and retrieval as prediction problems has recently emerged as a predominant approach in law intelligence. Focusing on the law article retrieval task, we present a deep learning framework named LamBERTa, which is designed for civil-law codes, and specifically trained on the Italian civil code. To our knowledge, this is the first study proposing an advanced approach to law article prediction for the Italian legal system based on a BERT (Bidirectional Encoder Representations from Transformers) learning framework, which has recently attracted increased attention among deep learning approaches, showing outstanding effectiveness in several natural language processing and learning tasks. We define LamBERTa models by fine-tuning an Italian pre-trained BERT on the Italian civil code or its portions, for law article retrieval as a classification task. One key aspect of our LamBERTa framework is that we conceived it to address an extreme classification scenario, which is characterized by a high number of classes, the few-shot learning problem, and the lack of test query benchmarks for Italian legal prediction tasks. To solve such issues, we define different methods for the unsupervised labeling of the law articles, which can in principle be applied to any law article code system. We provide insights into the explainability and interpretability of our LamBERTa models, and we present an extensive experimental analysis over query sets of different type, for single-label as well as multi-label evaluation tasks. Empirical evidence has shown the effectiveness of LamBERTa, and also its superiority against widely used deep-learning text classifiers and a few-shot learner conceived for an attribute-aware prediction task.